Machine Learning for Atrial Fibrillation Ablation

心房颤动消融的机器学习

• 批准号: 10115455
• 负责人: VICKI Stover HERTZBERG
• 金额: $ 11.25万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-08 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10115455
• 关键词: Ablation; Address; Adoption; Affect; Algorithms; Anticoagulation; Area; Arrhythmia; Atrial Fibrillation; Attention; Cardiac ablation; Cauterize; Cluster Analysis; Communities; Computer software; Data; Dementia; Development; Electronic Health Record; Evaluation; Foundations; Frequencies; Future; Goals; Health; Healthcare Systems; Heart failure; Lead; Learning; Left atrial structure; Machine Learning; Methods; Mining; Modeling; Outcome; Patient Selection; Patient-Focused Outcomes; Patients; Pattern; Performance; Predictive Factor; Procedures; Process; Public Health; Publishing; Quality of life; Recurrence; Research; Risk; Standardization; Stroke; Stroke prevention; Subgroup; System; Techniques; Time; Training; Validation; Work; adverse outcome; base; clinical phenotype; cohort; common treatment; cost; effective therapy; experience; improved; machine learning algorithm; machine learning method; open source; palliative; phenotypic data; predictive modeling; primary outcome; public health relevance; response; risk stratification; secondary analysis; sociodemographics; success; unsupervised learning

SUMMARY Affecting over 6 million people in the U.S., atrial fibrillation (AF), the most common cardiac arrhythmia, is a major public health concern. AF is costly to the health care system and leads to significant health consequences (e.g., stroke, heart failure, dementia, decreased quality of life). With time, AF patients experience increased frequency and duration of AF episodes. Random occurrence of sporadic AF episodes and the need for anticoagulation to prevent stroke make AF difficult to manage. Many AF patients seek out atrial fibrillation ablation (AFA) in order to improve quality of life and decrease AF episodes. AFA, cauterization of areas of the left atrium, is the most effective treatment for persistent / paroxysmal AF. AFA success rates vary, but many patients will not be AF-free following AFA. At leading AFA centers, AF-free rates at one and two years after initial AFA were 40% and 37%, respectively. Given the modest success rates of AFA, patient selection for this procedure should receive more attention. Sociodemographic and clinical phenotype data have been used to predict AFA response, but collectively they have poor predictive ability. The widespread adoption of electronic health record (EHR) systems presents a ripe opportunity for a paradigm shift for predicting AFA outcomes. A better understanding of patient specific factors predicting AFA outcome will inform patient selection for this procedure. To this end we propose to use machine learning techniques to develop predictive models for outcomes of primary AFA procedures, addressing the following specific aims and research questions: 1. Aim 1: Predict adverse AFA outcomes using machine learning. • How well do existing risk scores predict AFA complications prior to initial procedure? • Can a machine learning model trained on EHR data provide better prediction of AFA complications? 2. Aim 2: Data-driven AFA outcome subgroup identification. • Can cluster analysis identify useful subgroups based on outcome trajectory? • Are other unsupervised ML algorithms such as sequential pattern mining alternatives for analyzing patient outcome trajectories? 3. Aim 3: Develop an open-source software toolkit. This project will lay the foundation for future refinement of existing machine learning methods as well as development of new methods to improve prediction of AF recurrence following AFA.

总结 影响了美国600多万人，心房纤颤（AF）是最常见的心律失常， 公共卫生问题。AF对医疗保健系统来说是昂贵的，并导致严重的健康后果（例如， 中风、心力衰竭、痴呆、生活质量下降）。随着时间的推移，AF患者的频率增加 和AF发作的持续时间。偶发性房颤发作的随机发生和抗凝治疗的需要， 预防中风使房颤难以管理。许多房颤患者寻求房颤消融术（AFA）， 改善生活质量并减少房颤发作。AFA，左心房区域的烧灼，是最常见的 持续性/阵发性房颤的有效治疗。AFA的成功率各不相同，但许多患者不会完全消除房颤 在AFA之后。在领先的AFA中心，初始AFA后一年和两年的无AF率分别为40%和37%， 分别考虑到AFA的成功率不高，选择这种手术的患者应该得到更多的支持。 关注社会人口统计学和临床表型数据已被用于预测AFA反应，但 它们共同具有较差的预测能力。电子健康记录（EHR）系统的广泛采用 为预测AFA结果的范式转变提供了一个成熟的机会。更好地了解患者 预测AFA结果的特定因素将为该手术的患者选择提供信息。为此，我们建议 使用机器学习技术来开发初级AFA手术结果的预测模型， 针对以下具体目标和研究问题： 1.目标1：使用机器学习预测不良AFA结果。 ·现有的风险评分在多大程度上预测了初次手术前的AFA并发症？ ·在EHR数据上训练的机器学习模型能否更好地预测AFA并发症？ 2.目的2：数据驱动的AFA结局亚组识别。 ·聚类分析能否根据结果轨迹识别有用的亚组？ ·其他无监督ML算法（如序列模式挖掘）是否可用于分析 患者结局轨迹？ 3.目标3：开发一个开放源码软件工具包。 该项目将为未来改进现有的机器学习方法奠定基础， 开发新方法以改善AFA后AF复发的预测。